package net.adaptivebox.deps;

/**
 * Description: The description of agent with hybrid differential evolution and particle swarm.
 *
 * @ Author        Create/Modi     Note
 * Xiaofeng Xie    Jun 10, 2004
 * Xiaofeng Xie    Jul 01, 2008
 *
 * This library is free software; you can redistribute it and/or
 * modify it under the terms of the GNU Lesser General Public
 * License as published by the Free Software Foundation; either
 * version 2.1 of the License, or (at your option) any later version.
 *
 * This library is distributed in the hope that it will be useful,
 * but WITHOUT ANY WARRANTY; without even the implied warranty of
 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
 * Lesser General Public License for more details.
 *
 * Please acknowledge the author(s) if you use this code in any way.
 *
 * @version 1.0
 * @Since MAOS1.0
 *
 * @References:
 * [1] Zhang W J, Xie X F. DEPSO: hybrid particle swarm with differential
 * evolution operator. IEEE International Conference on Systems, Man & Cybernetics,
 * Washington D C, USA, 2003: 3816-3821
 * [2] X F Xie, W J Zhang. SWAF: swarm algorithm framework for numerical
 *     optimization. Genetic and Evolutionary Computation Conference (GECCO),
 *     Seattle, WA, USA, 2004: 238-250
 *     -> an agent perspective
 */

import net.adaptivebox.deps.behavior.AbsGTBehavior;
import net.adaptivebox.deps.behavior.DEGTBehavior;
import net.adaptivebox.deps.behavior.PSGTBehavior;
import net.adaptivebox.global.RandomGenerator;
import net.adaptivebox.goodness.IGoodnessCompareEngine;
import net.adaptivebox.knowledge.ILibEngine;
import net.adaptivebox.knowledge.Library;
import net.adaptivebox.knowledge.SearchPoint;
import net.adaptivebox.problem.ProblemEncoder;
import net.adaptivebox.space.BasicPoint;

public class DEPSAgent {

  // Describes the problem to be solved
  private ProblemEncoder problemEncoder;

  // Forms the goodness landscape
  private IGoodnessCompareEngine qualityComparator;

  // store the point that generated in current learning cycle
  private SearchPoint trailPoint;

  // temp variable
  private AbsGTBehavior selectGTBehavior;

  // the own memory: store the point that generated in old learning cycle
  private BasicPoint pold_t;

  // the own memory: store the point that generated in last learning cycle
  private BasicPoint pcurrent_t;

  // the own memory: store the personal best point
  private SearchPoint pbest_t;

  // Generate-and-test behaviors.
  private DEGTBehavior deGTBehavior;
  private PSGTBehavior psGTBehavior;

  private double switchP = 0.5;

  public DEPSAgent(ProblemEncoder encoder, DEGTBehavior deGTBehavior, PSGTBehavior psGTBehavior,
                   double switchP, IGoodnessCompareEngine comparer, SearchPoint pbest) {
      this.switchP = switchP;

      problemEncoder = encoder;

      qualityComparator = comparer;

      trailPoint = problemEncoder.getFreshSearchPoint();
      pold_t = problemEncoder.getFreshSearchPoint();
      pcurrent_t = problemEncoder.getFreshSearchPoint();
      pbest_t = pbest;

      this.deGTBehavior = deGTBehavior;
      this.deGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);

      this.psGTBehavior = psGTBehavior;
      this.psGTBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
  }

  public void setSpecComparator(IGoodnessCompareEngine comparer) {
    qualityComparator = comparer;
  }

  private AbsGTBehavior getGTBehavior() {
    if (RandomGenerator.doubleZeroOneRandom() < switchP) {
      return deGTBehavior;
    } else {
      return psGTBehavior;
    }
  }

  public void setGTBehavior(AbsGTBehavior gtBehavior) {
    gtBehavior.setMemPoints(pbest_t, pcurrent_t, pold_t);
  }

  public void generatePoint() {
    // generates a new point in the search space (S) based on
    // its memory and the library
    selectGTBehavior = getGTBehavior();
    selectGTBehavior.generateBehavior(trailPoint, problemEncoder);

    // evaluate into goodness information
    problemEncoder.evaluate(trailPoint);
  }

  public void learn() {
    selectGTBehavior.testBehavior(trailPoint, qualityComparator);
  }

  public SearchPoint getMGState() {
    return trailPoint;
  }
}